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Abstract

Background:
Cognitive impairment is a very common problem, especially in older individuals
with major impact on quality of life, daily functioning, and healthcare. Its
importance is expected to increase due to the demographic changes.
Neuroimaging is a rapidly developing field of neuroscience that provides an
opportunity to study brain mechanisms of cognitive impairment in vivo, which
may help in the development of new biomarkers and treatment strategies. The
application of advanced image processing to neuroimaging offers the potential
for diagnostically relevant analysis techniques, in particular for magnetic
resonance imaging (MRI).

Aim:
The primary aims of the project were to investigate brain mechanisms of
cognitive impairment in neurodegenerative diseases using computational
neuroimaging approaches and to assess their potential applicability in clinical
practice for detection, prediction and differential diagnosis of cognitive
impairment in the elderly.

Methods:
Five datasets of clinical and imaging data were used, including two large-scale
databases of Alzheimer’s disease (ADNI and AddNeuroMed).
In the papers I-II, Alzheimer’s disease was diagnosed according to the NINCDSADRDA
criteria.
Dementia with Lewy bodies (paper I) was diagnosed according to the revised
consensus criteria (1)
Image post-processing steps were performed within the surface- (papers I-III)
and voxel-based (paper IV) frameworks using the Freesurfer and SPM8,
respectively. Mass-univariate (papers III, IV) and multivariate (papers I, II and
IV) approaches were used. In the paper IV, an automated quantitative metaanalysis
was also performed using the Neurosynth software.

Results: Papers I-II: Optimizing image preprocessing and data analysis pipeline, we found that it is
possible to develop a computer-aided tool for detection (Sensitivity/Specificity =
88.6%/92.0%), prediction (Sensitivity/Specificity = 83.3%/81.3%) and
differential diagnosis (AD/DLB overall classification accuracy = 83.9%) of
degenerative diseases with good between-cohort robustness if imaging and
clinical protocols are carefully aligned. For the morphometric data, the use of
disease-specific brain parcellation schemes resulted in equivalent performance compared to normalized raw high-dimensional input, but required substantially
lesser tuning time and computation/memory resources. Better accuracy of the
models can be achieved by adding more biomarkers (e.g., ApoE genotype),
demographics, and improved disease verification strategies (e.g., post-mortem
diagnosis) for the data used as a training material for the classifiers.

The next two papers were focused on neural correlates of cognitive impairment
in PD that had to be investigated prior considering them within the framework of
computer-aided diagnosis.

Papers III-IV:
We found that Parkinson’s-related cognitive impairment affecting multiple
domains is associated with temporo-parietal and superior frontal thinning. On a
large-scale network level, better executive performance in PD is associated with
increased dorsal fronto-parietal cortical processing and inhibited subcortical and
primary sensory involvement when the subject is at resting state. This pattern is
positively influenced by the relative preservation of nigrostriatal dopaminergic
function. The pattern associated with better memory performance favors
prefronto-limbic processing, and does not reveal associations with presynaptic
striatal dopamine function.

Conclusions:
Cognitive impairment in the elderly has different brain profiles depending on the
predominant neurodegenerative pathology and cognitive functions affected. With
the use of automated computer-aided tools and advanced image processing
techniques, Alzheimer’s disease can be robustly identified, predicted two years
before the actual dementia onset and differentiated from dementia with Lewy
bodies. After certain modifications and adaptations for clinicians, the models can
be successfully incorporated into medical decision-support systems and be
evaluated in subsequent diagnostic clinical trials. The identified brain structural and functional profile associated with Parkinson’srelated
cognitive impairment is also robust and, holding strong diagnostic
potential, must be detectable using computer-aided systems of similar design, the
development of which is the matter of our future research. The development and
future elaboration of clinically realistic computer-aided systems for the diagnosis
of neurodegenerative diseases is an important topic for future research.

Has part(s)

Paper 1: Lebedev AV, Westman E, Beyer MK, Kramberger MG, Aguilar C, Pirtosek Z, Aarsland D. Multivariate classification of patients with Alzheimer's and dementia with Lewy bodies using high-dimensional cortical thickness measurements: an MRI surface-based morphometric study. Journal of Neurology, 2013. 260:1104-1115. The article is not available in BORA due to publisher restrictions. The published version is available at: http://dx.doi.org/10.1007/s00415-012-6768-z